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@INPROCEEDINGS{duda08miccai,
  author = {Jeffrey T Duda and Brian B Avants and Jane C Asmuth and Hui Zhang
	and Murray Grossman and James C Gee},
  title = {A Fiber Tractography Based Examination of Neurodegeneration on Language-Network
	Neuroanatomy},
  booktitle = {Workshop on Computational Diffusion MRI, Medical Image Computing
	and Computer-Assisted Intervention},
  year = {2008},
  pages = {191-198},
  month = {Sep},
  file = {full proceedings:http\://www.picsl.upenn.edu/cdmri08/proceedings.pdf:PDF},
  location = {New York, NY},
  owner = {jeff}
}
@INPROCEEDINGS{duda08cvpr,
  author = {Jeffrey T Duda and Brian B Avants and Junghoon Kim and Hui Zhang
	and S Patel and John Whyte and James C Gee},
  title = {Multivariate Analysis of Thalamo-Cortical Connectivity Loss in {TBI}},
  booktitle = {Computer Vision and Pattern Recognition},
  year = {2008},
  pages = {1-8},
  address = {Los Alamitos},
  month = {June},
  publisher = {IEEE Computer Society},
  doi = {10.1109/CVPRW.2008.4562992},
  location = {Anchorage, AK},
  owner = {stnava},
  timestamp = {2009.07.02}
}

@ARTICLE{Avants2008,
  author = {Brian Avants and Jeffrey T Duda and Junghoon Kim and Hui Zhang and
	John Pluta and James C Gee and John Whyte},
  title = {Multivariate analysis of structural and diffusion imaging in traumatic
	brain injury.},
  journal = {Acad Radiol},
  year = {2008},
  volume = {15},
  pages = {1360--1375},
  number = {11},
  month = {Nov},
  abstract = {RATIONALE AND OBJECTIVES: Diffusion tensor (DT) and T1 structural
	magnetic resonance images provide unique and complementary tools
	for quantifying the living brain. We leverage both modalities in
	a diffeomorphic normalization method that unifies analysis of clinical
	datasets in a consistent and inherently multivariate (MV) statistical
	framework. We use this technique to study MV effects of traumatic
	brain injury (TBI). MATERIALS AND METHODS: We contrast T1 and DT
	image-based measurements in the thalamus and hippocampus of 12 TBI
	survivors and nine matched controls normalized to a combined DT and
	T1 template space. The normalization method uses maps that are topology-preserving
	and unbiased. Normalization is based on the full tensor of information
	at each voxel and, simultaneously, the similarity between high-resolution
	features derived from T1 data. The technique is termed symmetric
	normalization for MV neuroanatomy (SyNMN). Voxel-wise MV statistics
	on the local volume and mean diffusion are assessed with Hotelling's
	T(2) test with correction for multiple comparisons. RESULTS: TBI
	significantly (false discovery rate P < .05) reduces volume and increases
	mean diffusion at coincident locations in the mediodorsal thalamus
	and anterior hippocampus. CONCLUSIONS: SyNMN reveals evidence that
	TBI compromises the limbic system. This TBI morphometry study and
	an additional performance evaluation contrasting SyNMN with other
	methods suggest that the DT component may aid normalization quality.},
  doi = {10.1016/j.acra.2008.07.007},
  institution = {Department of Radiology, University of Pennsylvania, Philadelphia,
	PA 19104, USA.},
  keywords = {Adult; Brain; Brain Injuries; Cohort Studies; Diffusion Magnetic Resonance
	Imaging; Echo-Planar Imaging; Female; Hippocampus; Humans; Image
	Processing, Computer-Assisted; Male; Middle Aged; Multivariate Analysis;
	Thalamus},
  owner = {stnava},
  pii = {S1076-6332(08)00395-4},
  pmid = {18995188},
  timestamp = {2009.02.14},
  url = {http://dx.doi.org/10.1016/j.acra.2008.07.007}
}
@ARTICLE{Avants2008a,
  author = {B. B. Avants and C. L. Epstein and M. Grossman and J. C. Gee},
  title = {Symmetric diffeomorphic image registration with cross-correlation:
	Evaluating automated labeling of elderly and neurodegenerative brain.},
  journal = {Med Image Anal},
  year = {2008},
  volume = {12},
  pages = {26--41},
  number = {1},
  month = {Feb},
  abstract = {One of the most challenging problems in modern neuroimaging is detailed
	characterization of neurodegeneration. Quantifying spatial and longitudinal
	atrophy patterns is an important component of this process. These
	spatiotemporal signals will aid in discriminating between related
	diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease
	(AD), which manifest themselves in the same at-risk population. Here,
	we develop a novel symmetric image normalization method (SyN) for
	maximizing the cross-correlation within the space of diffeomorphic
	maps and provide the Euler-Lagrange equations necessary for this
	optimization. We then turn to a careful evaluation of our method.
	Our evaluation uses gold standard, human cortical segmentation to
	contrast SyN's performance with a related elastic method and with
	the standard ITK implementation of Thirion's Demons algorithm. The
	new method compares favorably with both approaches, in particular
	when the distance between the template brain and the target brain
	is large. We then report the correlation of volumes gained by algorithmic
	cortical labelings of FTD and control subjects with those gained
	by the manual rater. This comparison shows that, of the three methods
	tested, SyN's volume measurements are the most strongly correlated
	with volume measurements gained by expert labeling. This study indicates
	that SyN, with cross-correlation, is a reliable method for normalizing
	and making anatomical measurements in volumetric MRI of patients
	and at-risk elderly individuals.},
  doi = {/j.media.2007.06.004},
  institution = {Department of Radiology, University of Pennsylvania, 3600 Market
	Street, Philadelphia, PA 19104, United States.},
  owner = {stnava},
  pii = {S1361-8415(07)00060-6},
  pmid = {17659998},
  timestamp = {2008.02.25},
  url = {http://dx.doi.org//j.media.2007.06.004}
}
@ARTICLE{Grossman2008,
  author = {M. Grossman and C. Anderson and A. Khan and B. Avants and L. Elman
	and L. McCluskey},
  title = {Impaired action knowledge in amyotrophic lateral sclerosis.},
  journal = {Neurology},
  year = {2008},
  volume = {71},
  pages = {1396--1401},
  number = {18},
  month = {Oct},
  abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative
	condition affecting the motor system, but recent work also shows
	more widespread cognitive impairment. This study examined performance
	on measures requiring knowledge of actions, and related performance
	to MRI cortical atrophy in ALS. METHODS: A total of 34 patients with
	ALS performed measures requiring word-description matching and associativity
	judgments with actions and objects. Voxel-based morphometry was used
	to relate these measures to cortical atrophy using high resolution
	structural MRI. RESULTS: Patients with ALS were significantly more
	impaired on measures requiring knowledge of actions than measures
	requiring knowledge of objects. Difficulty on measures requiring
	action knowledge correlated with cortical atrophy in motor cortex,
	implicating degraded knowledge of action features represented in
	motor cortex of patients with ALS. Performance on measures requiring
	object knowledge did not correlate with motor cortex atrophy. Several
	areas correlated with difficulty for both actions and objects, implicating
	these brain areas in components of semantic memory that are not dedicated
	to a specific category of knowledge. CONCLUSION: Patients with amyotrophic
	lateral sclerosis are impaired on measures involving action knowledge,
	and this appears to be due to at least two sources of impairment:
	degradation of knowledge about action features represented in motor
	cortex and impairment on multicategory cognitive components contributing
	more generally to semantic memory.},
  doi = {10.1212/01.wnl.0000319701.50168.8c},
  institution = {Department of Neurology-2 Gibson, Hospital of the University of Pennsylvania,
	3400 Spruce Street, Philadelphia, PA 19104-4283, USA. mgrossma@mail.med.upenn.edu},
  keywords = {Aged; Amyotrophic Lateral Sclerosis; Brain; Case-Control Studies;
	Cognition Disorder; Fema; Humans; Judgment; Knowledge; Magnetic Resonance
	Imaging; Male; Middle Aged; Motor Activity; Neuropsychological Tests;
	Pick Disease of the Brain; le; s},
  owner = {stnava},
  pii = {01.wnl.0000319701.50168.8c},
  pmid = {18784377},
  timestamp = {2009.06.30},
  url = {http://dx.doi.org/10.1212/01.wnl.0000319701.50168.8c}
}
@ARTICLE{Kim2008,
  author = {Junghoon Kim and Brian Avants and Sunil Patel and John Whyte and
	Branch H Coslett and John Pluta and John A Detre and James C Gee},
  title = {Structural consequences of diffuse traumatic brain injury: A large
	deformation tensor-based morphometry study.},
  journal = {Neuroimage},
  year = {2008},
  volume = {39},
  pages = {1014--1026},
  number = {3},
  month = {Feb},
  abstract = {Traumatic brain injury (TBI) is one of the most common causes of long-term
	disability. Despite the importance of identifying neuropathology
	in individuals with chronic TBI, methodological challenges posed
	at the stage of inter-subject image registration have hampered previous
	voxel-based MRI studies from providing a clear pattern of structural
	atrophy after TBI. We used a novel symmetric diffeomorphic image
	normalization method to conduct a tensor-based morphometry (TBM)
	study of TBI. The key advantage of this method is that it simultaneously
	estimates an optimal template brain and topology preserving deformations
	between this template and individual subject brains. Detailed patterns
	of atrophies are then revealed by statistically contrasting control
	and subject deformations to the template space. Participants were
	29 survivors of TBI and 20 control subjects who were matched in terms
	of age, gender, education, and ethnicity. Localized volume losses
	were found most prominently in white matter regions and the subcortical
	nuclei including the thalamus, the midbrain, the corpus callosum,
	the mid- and posterior cingulate cortices, and the caudate. Significant
	voxel-wise volume loss clusters were also detected in the cerebellum
	and the frontal/temporal neocortices. Volume enlargements were identified
	largely in ventricular regions. A similar pattern of results was
	observed in a subgroup analysis where we restricted our analysis
	to the 17 TBI participants who had no macroscopic focal lesions (total
	lesion volume >1.5 cm(3)). The current study confirms, extends, and
	partly challenges previous structural MRI studies in chronic TBI.
	By demonstrating that a large deformation image registration technique
	can be successfully combined with TBM to identify TBI-induced diffuse
	structural changes with greater precision, our approach is expected
	to increase the sensitivity of future studies examining brain-behavior
	relationships in the TBI population.},
  doi = {05},
  institution = {Moss Rehabilitation Research Institute, Albert Einstein Healthcare
	Network, Philadelphia, PA, USA.},
  owner = {stnava},
  pii = {S1053-8119(07)00901-9},
  pmid = {17999940},
  timestamp = {2008.02.25},
  url = {http://dx.doi.org/05}
}
@ARTICLE{Simon2008,
  author = {Tony J Simon and Zhongle Wu and Brian Avants and Hui Zhang and James
	C Gee and Glenn T Stebbins},
  title = {Atypical cortical connectivity and visuospatial cognitive impairments
	are related in children with chromosome 22q11.2 deletion syndrome.},
  journal = {Behav Brain Funct},
  year = {2008},
  volume = {4},
  pages = {25},
  abstract = {ABSTRACT: BACKGROUND: Chromosome 22q11.2 deletion syndrome is one
	of the most common genetic causes of cognitive impairment and developmental
	disability yet little is known about the neural bases of those challenges.
	Here we expand upon our previous neurocognitive studies by specifically
	investigating the hypothesis that changes in neural connectivity
	relate to cognitive impairment in children with the disorder. METHODS:
	Whole brain analyses of multiple measures computed from diffusion
	tensor image data acquired from the brains of children with the disorder
	and typically developing controls. We also correlated diffusion tensor
	data with performance on a visuospatial cognitive task that taps
	spatial attention. RESULTS: Analyses revealed four common clusters,
	in the parietal and frontal lobes, that showed complementary patterns
	of connectivity in children with the deletion and typical controls.
	We interpreted these results as indicating differences in connective
	complexity to adjoining cortical regions that are critical to the
	cognitive functions in which affected children show impairments.
	Strong, and similarly opposing patterns of correlations between diffusion
	values in those clusters and spatial attention performance measures
	considerably strengthened that interpretation. CONCLUSION: Our results
	suggest that atypical development of connective patterns in the brains
	of children with chromosome 22q11.2 deletion syndrome indicate a
	neuropathology that is related to the visuospatial cognitive impairments
	that are commonly found in affected individuals.},
  doi = {10.1186/1744-9081-4-25},
  institution = {M,I,N,D, Institute, University of California, Davis, 2825 50th Street,
	Sacramento, CA 95817, USA. tjsimon@ucdavis.edu.},
  owner = {stnava},
  pii = {1744-9081-4-25},
  pmid = {18559106},
  timestamp = {2008.10.06},
  url = {http://dx.doi.org/10.1186/1744-9081-4-25}
}

@ARTICLE{Pluta2009,
  author = {John Pluta and Brian B Avants and Simon Glynn and Suyash Awate and
	James C Gee and John A Detre},
  title = {Appearance and incomplete label matching for diffeomorphic template
	based hippocampus segmentation.},
  journal = {Hippocampus},
  year = {2009},
  volume = {19},
  pages = {565--571},
  number = {6},
  month = {Jun},
  abstract = {We present a robust, high-throughput, semiautomated template-based
	protocol for segmenting the hippocampus in temporal lobe epilepsy.
	The semiautomated component of this approach, which minimizes user
	effort while maximizing the benefit of human input to the algorithm,
	relies on "incomplete labeling." Incomplete labeling requires the
	user to quickly and approximately segment a few key regions of the
	hippocampus through a user-interface. Subsequently, this partial
	labeling of the hippocampus is combined with image similarity terms
	to guide volumetric diffeomorphic normalization between an individual
	brain and an unbiased disease-specific template, with fully labeled
	hippocampi. We solve this many-to-few and few-to-many matching problem,
	and gain robustness to inter and intrarater variability and small
	errors in user labeling, by embedding the template-based normalization
	within a probabilistic framework that examines both label geometry
	and appearance data at each label. We evaluate the reliability of
	this framework with respect to manual labeling and show that it increases
	minimum performance levels relative to fully automated approaches
	and provides high inter-rater reliability. Thus, this approach does
	not require expert neuroanatomical training and is viable for high-throughput
	studies of both the normal and the highly atrophic hippocampus.},
  doi = {10.1002/hipo.20619},
  institution = {d.upenn.edu},
  owner = {stnava},
  pmid = {19437413},
  timestamp = {2009.06.30},
  url = {http://dx.doi.org/10.1002/hipo.20619}
}


@ARTICLE{Aguirre2007,
  author = {Geoffrey K Aguirre and Andres M Komeromy and Artur V Cideciyan and
	David H Brainard and Tomas S Aleman and Alejandro J Roman and Brian
	B Avants and James C Gee and Marc Korczykowski and William W Hauswirth
	and Gregory M Acland and Gustavo D Aguirre and Samuel G Jacobson},
  title = {Canine and human visual cortex intact and responsive despite early
	retinal blindness from RPE65 mutation.},
  journal = {PLoS Med},
  year = {2007},
  volume = {4},
  pages = {e230},
  number = {6},
  month = {Jun},
  abstract = {BACKGROUND: RPE65 is an essential molecule in the retinoid-visual
	cycle, and RPE65 gene mutations cause the congenital human blindness
	known as Leber congenital amaurosis (LCA). Somatic gene therapy delivered
	to the retina of blind dogs with an RPE65 mutation dramatically restores
	retinal physiology and has sparked international interest in human
	treatment trials for this incurable disease. An unanswered question
	is how the visual cortex responds after prolonged sensory deprivation
	from retinal dysfunction. We therefore studied the cortex of RPE65-mutant
	dogs before and after retinal gene therapy. Then, we inquired whether
	there is visual pathway integrity and responsivity in adult humans
	with LCA due to RPE65 mutations (RPE65-LCA). METHODS AND FINDINGS:
	RPE65-mutant dogs were studied with fMRI. Prior to therapy, retinal
	and subcortical responses to light were markedly diminished, and
	there were minimal cortical responses within the primary visual areas
	of the lateral gyrus (activation amplitude mean +/- standard deviation
	[SD] = 0.07\% +/- 0.06\% and volume = 1.3 +/- 0.6 cm(3)). Following
	therapy, retinal and subcortical response restoration was accompanied
	by increased amplitude (0.18\% +/- 0.06\%) and volume (8.2 +/- 0.8
	cm(3)) of activation within the lateral gyrus (p < 0.005 for both).
	Cortical recovery occurred rapidly (within a month of treatment)
	and was persistent (as long as 2.5 y after treatment). Recovery was
	present even when treatment was provided as late as 1-4 y of age.
	Human RPE65-LCA patients (ages 18-23 y) were studied with structural
	magnetic resonance imaging. Optic nerve diameter (3.2 +/- 0.5 mm)
	was within the normal range (3.2 +/- 0.3 mm), and occipital cortical
	white matter density as judged by voxel-based morphometry was slightly
	but significantly altered (1.3 SD below control average, p = 0.005).
	Functional magnetic resonance imaging in human RPE65-LCA patients
	revealed cortical responses with a markedly diminished activation
	volume (8.8 +/- 1.2 cm(3)) compared to controls (29.7 +/- 8.3 cm(3),
	p < 0.001) when stimulated with lower intensity light. Unexpectedly,
	cortical response volume (41.2 +/- 11.1 cm(3)) was comparable to
	normal (48.8 +/- 3.1 cm(3), p = 0.2) with higher intensity light
	stimulation. CONCLUSIONS: Visual cortical responses dramatically
	improve after retinal gene therapy in the canine model of RPE65-LCA.
	Human RPE65-LCA patients have preserved visual pathway anatomy and
	detectable cortical activation despite limited visual experience.
	Taken together, the results support the potential for human visual
	benefit from retinal therapies currently being aimed at restoring
	vision to the congenitally blind with genetic retinal disease.},
  doi = {10.1371/journal.pmed.0040230},
  institution = {Department of Neurology, School of Medicine, University of Pennsylvania,
	Philadelphia, Pennsylvania, United States of America. aguirreg@mail.med.upenn.edu},
  keywords = {Adolescent; Adult; Animals; Blindness; Brain; Ca; Disease Models,
	Animal; Dogs; Eye Diseases, Hereditary; Eye Proteins; Female; Gene
	Therapy; Humans; Magnetic Resonance Imagin; Male; Mutation; Pigment
	Epithelium of Eye; Retina; Retinitis Pigmentosa; Visual Cortex; g;
	rrier Proteins},
  owner = {stnava},
  pii = {06-PLME-RA-1021},
  pmid = {17594175},
  timestamp = {2008.05.29},
  url = {http://dx.doi.org/10.1371/journal.pmed.0040230}
}

@PHDTHESIS{Avants2005,
  author = {B. Avants},
  title = {Shape optimizing diffeomorphisms for medical image analysis},
  school = {University of Pennsylvania},
  year = {2005}
}

@INPROCEEDINGS{Avants2005a,
  author = {B. Avants and J. Aguirre and J. Walker and J. C. Gee},
  title = {Unbiased Diffeomorphic Shape and Intensity Atlas Creation},
  booktitle = {ISMRM},
  year = {2005}
}

@CONFERENCE{Avants2007,
  author = {B. Avants and C. Anderson and M. Grossman},
  title = {Tauopathic Longitudinal Gray Matter Atrophy Predicts Declining Verbal
	Fluency: A Symmetric Normalization Study},
  booktitle = {Human Brain Mapping},
  year = {2007},
  owner = {stnava},
  timestamp = {2007.02.23}
}

@ARTICLE{Avants2007a,
  author = {Brian Avants and Chivon Anderson and Murray Grossman and James C
	Gee},
  title = {Spatiotemporal normalization for longitudinal analysis of gray matter
	atrophy in frontotemporal dementia.},
  journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput
	Assist Interv},
  year = {2007},
  volume = {10},
  pages = {303--310},
  number = {Pt 2},
  abstract = {We present a unified method, based on symmetric diffeomorphisms, for
	studying longitudinal neurodegeneration. Our method first uses symmetric
	diffeomorphic normalization to find a spatiotemporal parameterization
	of an individual's image time series. The second step involves mapping
	a representative image or set of images from the time series into
	an optimal template space. The template mapping is then combined
	with the intrasubject spatiotemporal map to enable pairwise statistical
	tests to be performed on a population of normalized time series images.
	Here, we apply this longitudinal analysis protocol to study the gray
	matter atrophy patterns induced by frontotemporal dementia (FTD).
	We sample our normalized spatiotemporal maps at baseline (time zero)
	and time one year to generate an annualized atrophy map (AAM) that
	estimates the annual effect of FTD. This spatiotemporal normalization
	enables us to locate neuroanatomical regions that consistently undergo
	significant annual gray matter atrophy across the population. We
	found the majority of annual atrophy to occur in the frontal and
	temporal lobes in our population of 20 subjects. We also found significant
	effects in the hippocampus, insula and cingulate gyrus. Our novel
	results, significant at p < 0.05 after false discovery rate correction,
	are represented in local template space but also assigned Talairach
	coordinates and Brodmann and Anatomical Automatic Labeling (AAL)
	labels. This paper shows the statistical power of symmetric diffeomorphic
	normalization for performing deformation-based studies of longitudinal
	atrophy.},
  institution = {Dept. of Radiology, University of Pennsylvania, Philadelphia, PA
	19104-6389, USA. avants@grasp.cis.upenn.edu},
  keywords = {Alg; Atrophy; Cerebral Cortex; Dementia; Humans; Image Enhancement;
	Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional;
	Longitudinal Studies; Magnetic Resonance Imaging; Neurons; Reproducibility
	of Results; Sensitivity and Specificity; orithms},
  owner = {stnava},
  pmid = {18044582},
  timestamp = {2008.02.25}
}

@CONFERENCE{Avants2007b,
  author = {B. Avants and C. Anderson and M. Grossman and J. C. Gee},
  title = {Symmetric normalization for patient-specific tracking of longitudinal
	change in frontotemporal dementia},
  booktitle = {Medical Image Computing and Computer Aided Intervention},
  year = {2007},
  volume = {2},
  pages = {303-310},
  owner = {stnava},
  timestamp = {2007.05.01}
}

@CONFERENCE{Avants2009a,
  author = {Avants, B. and Cook, P. A. and Pluta, J. nad Duda, J. T. and Rao,
	H. and Giannetta, J. and Hurt, H. and Das, S. and Gee, J.},
  title = {Multivariate Diffeomorphic Analysis of Longitudinal Increase in White
	Matter Directionality and Decrease in Cortical Thickness between
	Ages 14 and 18},
  booktitle = {Human Brain Mapping, 15th Annual Meeting, oral presentation.},
  year = {2009},
  owner = {stnava},
  timestamp = {2009.03.24}
}

@CONFERENCE{Avants2009b,
  author = {Avants, B. and Cook, P. A. and Pluta, J. nad Duda, J. T. and Rao,
	H. and Giannetta, J. and Hurt, H. and Das, S. and Gee, J.},
  title = {Follow-up on Long Term Effects of Prenatal Cocaine Exposure/Poly-Substance
	Abuse on the Young Adult Brain},
  booktitle = {Pediatric Academic Society, Annual Meeting},
  year = {2009},
  owner = {stnava},
  timestamp = {2009.03.24}
}

@ARTICLE{Avants2008c,
  author = {Brian Avants and Jeffrey T Duda and Junghoon Kim and Hui Zhang and
	John Pluta and James C Gee and John Whyte},
  title = {Multivariate analysis of structural and diffusion imaging in traumatic
	brain injury.},
  journal = {Acad Radiol},
  year = {2008},
  volume = {15},
  pages = {1360--1375},
  number = {11},
  month = {Nov},
  abstract = {RATIONALE AND OBJECTIVES: Diffusion tensor (DT) and T1 structural
	magnetic resonance images provide unique and complementary tools
	for quantifying the living brain. We leverage both modalities in
	a diffeomorphic normalization method that unifies analysis of clinical
	datasets in a consistent and inherently multivariate (MV) statistical
	framework. We use this technique to study MV effects of traumatic
	brain injury (TBI). MATERIALS AND METHODS: We contrast T1 and DT
	image-based measurements in the thalamus and hippocampus of 12 TBI
	survivors and nine matched controls normalized to a combined DT and
	T1 template space. The normalization method uses maps that are topology-preserving
	and unbiased. Normalization is based on the full tensor of information
	at each voxel and, simultaneously, the similarity between high-resolution
	features derived from T1 data. The technique is termed symmetric
	normalization for MV neuroanatomy (SyNMN). Voxel-wise MV statistics
	on the local volume and mean diffusion are assessed with Hotelling's
	T(2) test with correction for multiple comparisons. RESULTS: TBI
	significantly (false discovery rate P < .05) reduces volume and increases
	mean diffusion at coincident locations in the mediodorsal thalamus
	and anterior hippocampus. CONCLUSIONS: SyNMN reveals evidence that
	TBI compromises the limbic system. This TBI morphometry study and
	an additional performance evaluation contrasting SyNMN with other
	methods suggest that the DT component may aid normalization quality.},
  doi = {10.1016/j.acra.2008.07.007},
  institution = {Department of Radiology, University of Pennsylvania, Philadelphia,
	PA 19104, USA.},
  keywords = {Adult; Brain; Brain Injuries; Cohort Studies; Diffusion Magnetic Resonance
	Imaging; Echo-Planar Imaging; Female; Hippocampus; Humans; Image
	Processing, Computer-Assisted; Male; Middle Aged; Multivariate Analysis;
	Thalamus},
  owner = {stnava},
  pii = {S1076-6332(08)00395-4},
  pmid = {18995188},
  timestamp = {2009.02.14},
  url = {http://dx.doi.org/10.1016/j.acra.2008.07.007}
}

@CONFERENCE{Avants2007c,
  author = {B. Avants and J. T. Duda and H. Zhang and J. C. Gee},
  title = {Multivariate normalization with symmetric diffeomorphisms: An integrative
	approach},
  booktitle = {Medical Image Computing and Computer Aided Intervention},
  year = {2007},
  volume = {2},
  pages = {359-366},
  owner = {stnava},
  timestamp = {2007.05.01}
}

@CONFERENCE{Avants2007d,
  author = {B. Avants and C. L. Epstein and J. C. Gee},
  title = {Symmetric Shape Averaging in the Diffeomorphic Space},
  booktitle = {IEEE Symposium on Biomedical Imaging},
  year = {2007},
  owner = {stnava},
  timestamp = {2007.02.23}
}

@ARTICLE{Avants2006,
  author = {B. Avants and C. L. Epstein and J. C. Gee},
  title = {Geodesic image normalization in the space of diffeomorphisms},
  journal = {Mathematical Foundations of Computational Anatomy},
  year = {2006},
  pages = {125-133}
}

@INPROCEEDINGS{Avants2005b,
  author = {B. Avants and C. L. Epstein and J. C. Gee},
  title = {Geodesic image interpolation: {P}arameterizing and interpolating
	spatiotemporal images},
  booktitle = {ICCV Workshop on Variational and Level Set Methods},
  year = {2005},
  pages = {247-258}
}

@UNPUBLISHED{Avants2004,
  author = {B. Avants and C. L. Epstein and J. C. Gee},
  title = {A method for conformally mapping simply connected domains to the
	unit disc},
  note = {in preparation},
  year = {2004}
}

@ARTICLE{Avants2004a,
  author = {B. Avants and J.C. Gee},
  title = {Geodesic estimation for large deformation anatomical shape and intensity
	averaging},
  journal = {Neuroimage},
  year = {2004},
  volume = {Suppl. 1},
  pages = {S139-150}
}
@ARTICLE{Avants2004a,
  author = {B. Avants and J.C. Gee},
  title = {Geodesic estimation for large deformation anatomical shape and intensity
	averaging},
  journal = {Neuroimage},
  year = {2004},
  volume = {Suppl. 1},
  pages = {S139-150}
}

@ARTICLE{Avants2009c,
  author = {B. Avants and P. Yushkevich and J. Pluta and J. C. Gee},
  title = {The optimal template effect in studies of hippocampus in diseased populations},
  journal = {Neuroimage},
  year = {2009},
  pages = {in press}
}

@INPROCEEDINGS{Avants2003,
  author = {B. Avants and J.C. Gee},
  title = {Continuous Curve Matching with Scale-Space Curvature and Extrema-Based
	Scale Selection},
  booktitle = {Scale-Space Theories in Computer Vision},
  year = {2003},
  pages = {798-813},
  note = {L. Griffin editor, Heidelberg:Springer-Verlag, LNCS}
}

@ARTICLE{Avants2003a,
  author = {Brian Avants and James Gee},
  title = {The shape operator for differential analysis of images.},
  journal = {Inf Process Med Imaging},
  year = {2003},
  volume = {18},
  pages = {101--113},
  month = {Jul},
  abstract = {This work provides a new technique for surface oriented volumetric
	image analysis. The method makes no assumptions about topology, instead
	constructing a local neighborhood from image information, such as
	a segmentation or edge map, to define a surface patch. Neighborhood
	constructions using extrinsic and intrinsic distances are given.
	This representation allows one to estimate differential properties
	directly from the image's Gauss map. We develop a novel technique
	for this purpose which estimates the shape operator and yields both
	principal directions and curvatures. Only first derivatives need
	be estimated, making the method numerically stable. We show the use
	of these measures for multi-scale classification of image structure
	by the mean and Gaussian curvatures. Finally, we propose to register
	image volumes by surface curvature. This is particularly useful when
	geometry is the only variable. To illustrate this, we register binary
	segmented data by surface curvature, both rigidly and non-rigidly.
	A novel variant of Demons registration, extensible for use with differentiable
	similarity metrics, is also applied for deformable curvature-driven
	registration of medical images.},
  institution = {University of Pennsylvania, Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu},
  keywords = {Algorithms; Animals; Brain; Computer Simulation; Humans; Image Enhancement;
	Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional;
	Magnetic Resonance Imaging; Models, Biological; Models, Statistical;
	Pan troglodytes; Pattern Recognition, Automated; Subtraction Technique},
  owner = {stnava},
  pmid = {15344450},
  timestamp = {2008.05.29}
}

@ARTICLE{Avants2002,
  author = {B. Avants and J.C. Gee},
  title = {Morphometry of Brain Curve Anatomy from Similarity Invariant Parametric
	Matching Incorporating Global Topology},
  journal = {ISMRM 10th Scientific Meeting and Exhibition},
  year = {2002}
}

@ARTICLE{Avants2004d,
  author = {B. Avants and J. Gee and P. T. Schoenemann and R. L. Holloway and
	J. E. Lewis and J. Monge},
  title = {Validation of Plaster Endocast Morphology through {3D CT} Image Analysis},
  journal = {American Journal of Physical Anthropology},
  year = {2004},
  volume = {123},
  pages = {Suppl. 38:56}
}

@INPROCEEDINGS{Avants2002a,
  author = {B. Avants and J. C. Gee},
  title = {Soft parametric curve matching in scale space},
  booktitle = {Proc. SPIE Medical Imaging 2002: Image Processing},
  year = {2002},
  editor = {M. Fitzpatrick and M. Sonka},
  pages = {1139-1150},
  address = {Bellingham, WA},
  organization = {SPIE}
}

@INCOLLECTION{Avants2003b,
  author = {B. Avants and J. C. Gee},
  title = {Formulation and evaluation of variational curve matching with prior
	constraints},
  booktitle = {Biomedical Image Registration},
  publisher = {Springer-Verlag},
  year = {2003},
  editor = {J. C. Gee and J. B. A. Maintz and M. W. Vannier},
  pages = {21-30},
  address = {Heidelberg}
}

@ARTICLE{Avants2004g,
  author = {Brian Avants and James C Gee},
  title = {Geodesic estimation for large deformation anatomical shape averaging
	and interpolation.},
  journal = {Neuroimage},
  year = {2004},
  volume = {23 Suppl 1},
  pages = {S139--S150},
  abstract = {The goal of this research is to promote variational methods for anatomical
	averaging that operate within the space of the underlying image registration
	problem. This approach is effective when using the large deformation
	viscous framework, where linear averaging is not valid, or in the
	elastic case. The theory behind this novel atlas building algorithm
	is similar to the traditional pairwise registration problem, but
	with single image forces replaced by average forces. These group
	forces drive an average transport ordinary differential equation
	allowing one to estimate the geodesic that moves an image toward
	the mean shape configuration. This model gives large deformation
	atlases that are optimal with respect to the shape manifold as defined
	by the data and the image registration assumptions. We use the techniques
	in the large deformation context here, but they also pertain to small
	deformation atlas construction. Furthermore, a natural, inherently
	inverse consistent image registration is gained for free, as is a
	tool for constant arc length geodesic shape interpolation. The geodesic
	atlas creation algorithm is quantitatively compared to the Euclidean
	anatomical average to elucidate the need for optimized atlases. The
	procedures generate improved average representations of highly variable
	anatomy from distinct populations.},
  doi = {10.1016/j.neuroimage.2004.07.010},
  institution = {University of Pennsylvania, Philadelphia, PA 19104, USA. avants@grasp.cis.upenn.edu},
  keywords = {Algorithms; Animals; Brain; Brain Mapping; Databases, Factual; Humans;
	Linear Models; Magnetic Resonance Imaging; Models, Anatomic; Models,
	Statistical; Pan troglodytes; Population},
  owner = {stnava},
  pii = {S1053-8119(04)00375-1},
  pmid = {15501083},
  timestamp = {2008.05.29},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2004.07.010}
}

@INPROCEEDINGS{Avants2002b,
  author = {B. Avants and J. C. Gee},
  title = {Robust rotations between anatomical curves},
  booktitle = {IEEE International Symposium on Biomedical Imaging},
  year = {2002},
  pages = {337-340},
  address = {Piscataway, NJ},
  publisher = {IEEE Press}
}

@INPROCEEDINGS{Avants2006a,
  author = {B. Avants and J. C. Gee and J. Giannetta and D. Shera and H. Hurt},
  title = {Brain imaging: Brain Imaging: Structural differences between adolescent
	subjects with gestational cocaine exposure (COC) and controls (CON)},
  booktitle = {Poster Symposium Presentation. San Francisco, CA, April 30, 2006;
	3870.6, Pediatr Res.},
  year = {2006}
}

@ARTICLE{Avants2007e,
  author = {B. Avants and M. Grossman and J. C. Gee},
  title = {Symmetric Diffeomorphic Image Registration: Evaluating Automated
	Labeling of Elderly and Neurodegenerative Cortex},
  journal = {Medical Image Analysis},
  year = {2007},
  note = {in press},
  optpages = {in press}
}

@ARTICLE{Avants2006b,
  author = {B. Avants and M. Grossman and J. C. Gee},
  title = {Symmetric Diffeomorphic Image Registration: Evaluating Automated
	Labeling of Elderly and Neurodegenerative Cortex and Frontal Lobe},
  journal = {WBIR},
  year = {2006},
  pages = {50-57}
}

@ARTICLE{Avants2005d,
  author = {Brian Avants and Murray Grossman and James C Gee},
  title = {The correlation of cognitive decline with frontotemporal dementia
	induced annualized gray matter loss using diffeomorphic morphometry.},
  journal = {Alzheimer Dis Assoc Disord},
  year = {2005},
  volume = {19 Suppl 1},
  pages = {S25--S28},
  abstract = {This study uses large deformation medical image registration to analyze,
	in a disease-specific normalized space, the annual rate of gray matter
	atrophy caused by frontotemporal dementia (FTD) and its correlation
	with cognitive decline. The analysis consists of three parts. First,
	a labeled structural MRI atlas is deformed into the shape of an average
	FTD brain. Second, annualized FTD-related atrophy of gray matter
	structures is estimated for each patient in the database. Third,
	the group-wise annualized atrophy rate caused by FTD is correlated,
	for each gray matter voxel, with declining performance on cognitive
	tests. This study gives insight into the relationship between FTD-related
	progressive cortical atrophy and loss in cognitive function.},
  institution = {University of Pennsylvania School of Medicine, Philadelphia, 19104-6389,
	USA. avants@grasp.cis.upenn.edu},
  keywords = {Aged; Atrophy; Cerebral Cortex; Cognition Disorders; Disease Progression;
	Humans; Image Processing, Computer-Assisted; Longitudinal Studies;
	Magnetic Resonance Imaging; Middle Aged; Pick Disease of the Brain},
  owner = {stnava},
  pii = {00002093-200510001-00006},
  pmid = {16317254},
  timestamp = {2008.03.22}
}

@ARTICLE{Avants2009,
  author = {Brian Avants and Alea Khan and Leo McCluskey and Lauren Elman and
	Murray Grossman},
  title = {Longitudinal cortical atrophy in amyotrophic lateral sclerosis with
	frontotemporal dementia.},
  journal = {Arch Neurol},
  year = {2009},
  volume = {66},
  pages = {138--139},
  number = {1},
  month = {Jan},
  doi = {10.1001/archneurol.2008.542},
  owner = {stnava},
  pii = {66/1/138},
  pmid = {19139315},
  timestamp = {2009.02.14},
  url = {http://dx.doi.org/10.1001/archneurol.2008.542}
}

@INCOLLECTION{Avants2001,
  author = {Avants, B. and Siquiera, M. and Gee, J. C.},
  title = {Computing match functions for curves in R2 and R3 by refining polyline
	approximations},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention},
  publisher = {Springer-Verlag},
  year = {2001},
  editor = {Niessen, W. and Viergever, M.},
  pages = {1178-1179},
  address = {Heidelberg}
}

@ARTICLE{Avants1999,
  author = {B. Avants and D. Soodak and G. Ruppeiner},
  title = {Measuring the electrical conductivity of the earth},
  journal = {American Journal of Physics},
  year = {1999},
  volume = {67},
  pages = {593-598},
  number = {7}
}

@ARTICLE{Avants2000,
  author = {B. Avants and J. Williams},
  title = {An adaptive minimal path generation technique for vessel tracking
	in CTA/CE-MRA volume images},
  journal = {Medical Image Computing and Computer Assisted Intervention 2000},
  year = {2000},
  pages = {707-716},
  note = {S. Delp and A. DiGioia and B. Jaramaz, eds., Heidelberg:Springer-Verlag,
	LNCS 1935, {\em {\bf 2004}expanded as a chapter in {\bf Quantitative
	Vessel Analysis} book available from CRC press}}
}

@ARTICLE{Avants2007g,
  author = {B. Avants and P. Yushkevich and S. Awate and J. C. Gee and J. Detra
	and M. Korczykowski},
  title = {Optimal Template Creation with Symmetric Diffeomorphisms: Evaluation
	of the Template Effect},
  journal = {Medical Image Analysis},
  year = {2007},
  pages = {sumbitted},
  owner = {stnava},
  timestamp = {2007.10.19}
}

@ARTICLE{Avants2007h,
  author = {B. B. Avants and J. T. Duda and H. Zhang and J. C. Gee},
  title = {Multivariate normalization with symmetric diffeomorphisms for multivariate
	studies.},
  journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput
	Assist Interv},
  year = {2007},
  volume = {10},
  pages = {359--366},
  number = {Pt 1},
  abstract = {Current clinical and research neuroimaging protocols acquire images
	using multiple modalities, for instance, T1, T2, diffusion tensor
	and cerebral blood flow magnetic resonance images (MRI). These multivariate
	datasets provide unique and often complementary anatomical and physiological
	information about the subject of interest. We present a method that
	uses fused multiple modality (scalar and tensor) datasets to perform
	intersubject spatial normalization. Our multivariate approach has
	the potential to eliminate inconsistencies that occur when normalization
	is performed on each modality separately. Furthermore, the multivariate
	approach uses a much richer anatomical and physiological image signature
	to infer image correspondences and perform multivariate statistical
	tests. In this initial study, we develop the theory for Multivariate
	Symmetric Normalization (MVSyN), establish its feasibility and discuss
	preliminary results on a multivariate statistical study of 22q deletion
	syndrome.},
  institution = {Penn Image Computing and Science Laboratory, University of Pennsylvania,
	Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu},
  keywords = {Adult; Algorithms; Artificial Intelligence; Brain; Demyelinating Diseases;
	DiGeorge Syndrome; Diffusion Magnetic Resonance Imaging; Humans;
	Image Enhancement; Image Interpretation, Computer-Assisted; Imaging,
	Three-Dimensional; Multivariate Analysis; Pattern Recognition, Automated;
	Reproducibility of Results; Sensitivity and Specificity; Subtraction
	Technique},
  owner = {stnava},
  pmid = {18051079},
  timestamp = {2008.02.25}
}

@ARTICLE{Avants2008b,
  author = {B. B. Avants and C. L. Epstein and M. Grossman and J. C. Gee},
  title = {Symmetric diffeomorphic image registration with cross-correlation:
	Evaluating automated labeling of elderly and neurodegenerative brain.},
  journal = {Med Image Anal},
  year = {2008},
  volume = {12},
  pages = {26--41},
  number = {1},
  month = {Feb},
  abstract = {One of the most challenging problems in modern neuroimaging is detailed
	characterization of neurodegeneration. Quantifying spatial and longitudinal
	atrophy patterns is an important component of this process. These
	spatiotemporal signals will aid in discriminating between related
	diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease
	(AD), which manifest themselves in the same at-risk population. Here,
	we develop a novel symmetric image normalization method (SyN) for
	maximizing the cross-correlation within the space of diffeomorphic
	maps and provide the Euler-Lagrange equations necessary for this
	optimization. We then turn to a careful evaluation of our method.
	Our evaluation uses gold standard, human cortical segmentation to
	contrast SyN's performance with a related elastic method and with
	the standard ITK implementation of Thirion's Demons algorithm. The
	new method compares favorably with both approaches, in particular
	when the distance between the template brain and the target brain
	is large. We then report the correlation of volumes gained by algorithmic
	cortical labelings of FTD and control subjects with those gained
	by the manual rater. This comparison shows that, of the three methods
	tested, SyN's volume measurements are the most strongly correlated
	with volume measurements gained by expert labeling. This study indicates
	that SyN, with cross-correlation, is a reliable method for normalizing
	and making anatomical measurements in volumetric MRI of patients
	and at-risk elderly individuals.},
  doi = {/j.media.2007.06.004},
  institution = {Department of Radiology, University of Pennsylvania, 3600 Market
	Street, Philadelphia, PA 19104, United States.},
  owner = {stnava},
  pii = {S1361-8415(07)00060-6},
  pmid = {17659998},
  timestamp = {2008.02.25},
  url = {http://dx.doi.org//j.media.2007.06.004}
}

@INPROCEEDINGS{Avants2006c,
  author = {B. B. Avants and J. Giannetta and J. C. Gee and H. Hurt and J. Wang},
  title = {Analyzing Long Term Effects of Cocaine Exposure on Adolescent Brain
	Structure with Symmetric Diffeomorphisms},
  booktitle = {Mathematical Methods in Biomedical Image Analysis, New York City,
	NY},
  year = {2006}
}

@ARTICLE{Avants2007i,
  author = {Brian B Avants and Hallam Hurt and Joan M Giannetta and Charles L
	Epstein and David M Shera and Hengyi Rao and Jiongjiong Wang and
	James C Gee},
  title = {Effects of heavy in utero cocaine exposure on adolescent caudate
	morphology.},
  journal = {Pediatr Neurol},
  year = {2007},
  volume = {37},
  pages = {275--279},
  number = {4},
  month = {Oct},
  abstract = {We assess the effects of in utero cocaine and polysubstance exposure
	on the adolescent caudate nucleus through high-resolution magnetic
	resonance imaging. Cocaine exposure may compromise the developing
	brain through disruption of neural ontogeny in dopaminergic systems,
	effects secondary to fetal hypoxemia, or altered cerebrovascular
	reactivity. Cocaine exposure may also lead to neonatal lesions in
	the caudate. However, long-term or latent effects of intrauterine
	cocaine exposure are rarely found. We use T(1)-weighted magnetic
	resonance imaging to quantify caudate nucleus morphology in matched
	control and exposed groups. The literature suggests that in utero
	cocaine exposure consequences in adolescents may be subtle, or masked
	by other variables. Our comparison focuses on contrasting the control
	group with high-exposure subjects (mothers who reported 2 median
	of 117 days of cocaine use during pregnancy; 82\% tested positive
	for cocaine use at term). We use advanced image registration and
	segmentation tools to quantify left and right caudate morphology.
	Our results indicate that the caudate is significantly larger in
	controls versus subjects (P < 0.0025), implying cocaine exposure-related
	detriments to the dopaminergic system. The right (P < 0.025) and
	left (P < 0.035) caudate, studied independently, show the same significant
	trend. Permutation testing and the false discovery rate were used
	to assess significance.},
  doi = {10.1016/j.pediatrneurol.2007.06.012},
  institution = {Department of Radiology, University of Pennsylvania, Philadelphia,
	Pennsylvania 19104, USA. avants@grasp.cis.upenn.edu},
  keywords = {Adolescent; Caudate Nucleus; Cocaine; Cohort Studies; Dopamine Uptake
	Inhibitors; Dose-Response Relationship, Drug; Female; Humans; Magnetic
	Resonance Imaging; Male; Pregnancy; Prenatal Exposure Delayed Effects},
  owner = {stnava},
  pii = {S0887-8994(07)00328-1},
  pmid = {17903672},
  timestamp = {2008.02.25},
  url = {http://dx.doi.org/10.1016/j.pediatrneurol.2007.06.012}
}

@ARTICLE{Avants2006d,
  author = {Brian B Avants and P. Thomas Schoenemann and James C Gee},
  title = {Lagrangian frame diffeomorphic image registration: Morphometric comparison
	of human and chimpanzee cortex.},
  journal = {Med Image Anal},
  year = {2006},
  volume = {10},
  pages = {397--412},
  number = {3},
  month = {Jun},
  abstract = {We develop a novel Lagrangian reference frame diffeomorphic image
	and landmark registration method. The algorithm uses the fixed Langrangian
	reference frame to define the map between coordinate systems, but
	also generates and stores the inverse map from the Eulerian to the
	Lagrangian frame. Computing both maps allows facile computation of
	both Eulerian and Langrangian quantities. We apply this algorithm
	to estimating a putative evolutionary change of coordinates between
	a population of chimpanzee and human cortices. Inter-species functional
	homologues fix the map explicitly, where they are known, while image
	similarities guide the alignment elsewhere. This map allows detailed
	study of the volumetric change between chimp and human cortex. Instead
	of basing the inter-species study on a single species atlas, we diffeomorphically
	connect the mean shape and intensity templates for each group. The
	human statistics then map diffeomorphically into the space of the
	chimpanzee cortex providing a comparison between species. The population
	statistics show a significant doubling of the relative prefrontal
	lobe size in humans, as compared to chimpanzees.},
  doi = {10.1016/j.media.2005.03.005},
  institution = {Department of Bioengineering, University of Pennsylvania, Philadelphia,
	PA 19104-6389, USA. avants@grasp.cis.upenn.edu},
  keywords = {Algorithms; Animals; Anthropometry; Cerebral Cortex; Humans; Image
	Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional;
	Information Storage and Retrieval; Magnetic Resonance Imaging; Organ
	Size; Pan troglodytes; Reproducibility of Results; Sensitivity and
	Specificity; Species Specificity; Subtraction Technique},
  owner = {stnava},
  pii = {S1361-8415(05)00041-1},
  pmid = {15948659},
  timestamp = {2008.05.29},
  url = {http://dx.doi.org/10.1016/j.media.2005.03.005}
}

@ARTICLE{Cook2005,
  author = {P. A. Cook and H. Zhang and B. B. Avants and P. Yushkevich and D.
	C. Alexander and J. C. Gee and O. Ciccarelli and A. J. Thompson},
  title = {An automated approach to connectivity-based partitioning of brain
	structures.},
  journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput
	Assist Interv},
  year = {2005},
  volume = {8},
  pages = {164--171},
  number = {Pt 1},
  abstract = {We present an automated approach to the problem of connectivity-based
	partitioning of brain structures using diffusion imaging. White-matter
	fibres connect different areas of the brain, allowing them to interact
	with each other. Diffusion-tensor MRI measures the orientation of
	white-matter fibres in vivo, allowing us to perform connectivity-based
	partitioning non-invasively. Our new approach leverages atlas-based
	segmentation to automate anatomical labeling of the cortex. White-matter
	connectivities are inferred using a probabilistic tractography algorithm
	that models crossing pathways explicitly. The method is demonstrated
	with the partitioning of the corpus callosum of eight healthy subjects.},
  institution = {Centre for Medical Image Computing, Department of Computer Science,
	University College London, UK.},
  keywords = {Algorithms; Artificial Intelligence; Corpus Callosum; Diffusion Magnetic
	Resonance Imaging; Humans; Image Enhancement; Image Interpretation,
	Computer-Assisted; Imaging, Three-Dimensional; Nerve Fibers, Myelinated;
	Pattern Recognition, Automated; Reproducibility of Results; Sensitivity
	and Specificity},
  owner = {stnava},
  pmid = {16685842},
  timestamp = {2008.03.22}
}

@CONFERENCE{Das2007,
  author = {S. Das and B. Avants and C. Anderson and M. Grossman},
  title = {Longitudinal Study of Gray Matter Thickness Using Topologically Consistent
	Cortical Models},
  booktitle = {Human Brain Mapping},
  year = {2007},
  owner = {stnava},
  timestamp = {2007.02.23}
}

@INPROCEEDINGS{Das2007a,
  author = {S. Das and B. Avants and M. Grossman and J. C. Gee},
  title = {Measuring Cortical Thickness Using Image Domain Local Surface Models:
	Application to Longitudinal Study of Atrophy in FTD Spectrum Disorders},
  booktitle = {submitted, Medical Image Computing and Computer Aided Intervention},
  year = {2007}
}

@CONFERENCE{Das2007b,
  author = {S. Das and B. Avants and M. Grossman and J. C. Gee},
  title = {Measuring Cortical Thickness Using An Image Domain Local Surface
	Model And Topology Preserving Segmentation},
  booktitle = {MMBIA 2007},
  year = {2007},
  owner = {stnava},
  timestamp = {2007.10.19}
}

@ARTICLE{Das2009,
  author = {Sandhitsu R Das and Brian B Avants and Murray Grossman and James
	C Gee},
  title = {Registration based cortical thickness measurement.},
  journal = {Neuroimage},
  year = {2009},
  volume = {45},
  pages = {867--879},
  number = {3},
  month = {Apr},
  abstract = {Cortical thickness is an important biomarker for image-based studies
	of the brain. A diffeomorphic registration based cortical thickness
	(DiReCT) measure is introduced where a continuous one-to-one correspondence
	between the gray matter-white matter interface and the estimated
	gray matter-cerebrospinal fluid interface is given by a diffeomorphic
	mapping in the image space. Thickness is then defined in terms of
	a distance measure between the interfaces of this sheet like structure.
	This technique also provides a natural way to compute continuous
	estimates of thickness within buried sulci by preventing opposing
	gray matter banks from intersecting. In addition, the proposed method
	incorporates neuroanatomical constraints on thickness values as part
	of the mapping process. Evaluation of this method is presented on
	synthetic images. As an application to brain images, a longitudinal
	study of thickness change in frontotemporal dementia (FTD) spectrum
	disorder is reported.},
  doi = {10.1016/j.neuroimage.2008.12.016},
  institution = {Department of Radiology, University of Pennsylvania School of Medicine,
	Philadelphia, PA, USA. sudas@seas.upenn.edu},
  owner = {stnava},
  pii = {S1053-8119(08)01278-0},
  pmid = {19150502},
  timestamp = {2009.04.02},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2008.12.016}
}

@INPROCEEDINGS{Dubb2002,
  author = {A. Dubb and B. Avants and R. Gur and J. C. Gee},
  title = {Shape characterization of the corpus callosum in {S}chizophrenia
	using template deformation},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention},
  year = {2002},
  editor = {R. Kikinis},
  pages = {381-388},
  address = {Heidelberg},
  publisher = {Springer-Verlag}
}

@ARTICLE{Dubb2003,
  author = {Abraham Dubb and Ruben Gur and Brian Avants and James Gee},
  title = {Characterization of sexual dimorphism in the human corpus callosum.},
  journal = {Neuroimage},
  year = {2003},
  volume = {20},
  pages = {512--519},
  number = {1},
  month = {Sep},
  abstract = {Despite decades of research, there is still no agreement over the
	presence of gender-based morphologic differences in the human corpus
	callosum. We approached the problem using a highly precise computational
	technique for shape comparison. Starting with a prospectively acquired
	sample of cranial MRIs of healthy volunteers (age ranges 18-84),
	the variations of individual callosa are quantified with respect
	to a reference callosum shape in the form of Jacobian determinant
	maps derived from the geometric transformations that map the reference
	callosum into anatomic alignment with the subject callosa. Voxelwise
	t tests performed over the determinant values demonstrated that females
	had a larger splenium than males (P < 0.001 uncorrected for multiple
	comparisons) while males possessed a larger genu (P < 0.001). In
	addition, pointwise Pearson plots using age as a correlate showed
	a different pattern of age-related changes in male and female callosa,
	with female splenia tending to expand more with age, while the male
	genu tended to contract. Our results demonstrate significant morphologic
	differences in the corpus callosum between genders and a possible
	sex difference in the neuro-developmental cycle.},
  institution = {Department of Bioengineering, Psychiatry, and Radiology, University
	of Pennsylvania, Philadelphia, PA 19104-6389, USA. adubb@grasp.cis.upenn.edu},
  keywords = {Adolescent; Adult; Algorithms; Cluster Analysis; Corpus Callosum;
	Female; Humans; Magnetic Resonance Imaging; Male; Prospective Studies;
	Schizophrenia; Sex Characteristics},
  owner = {stnava},
  pii = {S1053811903003136},
  pmid = {14527611},
  timestamp = {2008.05.29}
}

@ARTICLE{Fan2007,
  author = {Yong Fan and Hengyi Rao and Hallam Hurt and Joan Giannetta and Marc
	Korczykowski and David Shera and Brian B Avants and James C Gee and
	Jiongjiong Wang and Dinggang Shen},
  title = {Multivariate examination of brain abnormality using both structural
	and functional MRI.},
  journal = {Neuroimage},
  year = {2007},
  volume = {36},
  pages = {1189--1199},
  number = {4},
  month = {Jul},
  abstract = {A multivariate classification approach has been presented to examine
	the brain abnormalities, i.e., due to prenatal cocaine exposure,
	using both structural and functional brain images. First, a regional
	statistical feature extraction scheme was adopted to capture discriminative
	features from voxel-wise morphometric and functional representations
	of brain images, in order to reduce the dimensionality of the features
	used for classification, as well as to achieve the robustness to
	registration error and inter-subject variations. Then, this feature
	extraction method was used in conjunction with a hybrid feature selection
	method and a nonlinear support vector machine for the classification
	of brain abnormalities. This brain classification approach has been
	applied to detecting the brain abnormality associated with prenatal
	cocaine exposure in adolescents. A promising classification performance
	was achieved on a data set of 49 subjects (24 normal and 25 prenatally
	cocaine-exposed teenagers), with a leave-one-out cross-validation.
	Experimental results demonstrated the efficacy of our method, as
	well as the importance of incorporating both structural and functional
	images for brain classification. Moreover, spatial patterns of group
	difference derived from the constructed classifier were mostly consistent
	with the results of the conventional statistical analysis method.
	Therefore, the proposed approach provided not only a multivariate
	classification method for detecting brain abnormalities, but also
	an alternative way for group analysis of multimodality images.},
  doi = {10.1016/j.neuroimage.2007.04.009},
  institution = {Department of Radiology, University of Pennsylvania, PA 19104, USA.
	yong.fan@uphs.upenn.edu},
  keywords = {Adolescent; Algorithms; Artificial Intelligence; Brain; Cocaine; Female;
	Humans; Image Enhancement; Image Processing, Computer-Assisted; Magnetic
	Resonance Imaging; Multivariate Analysis; Nonlinear Dynamics; Pregnancy;
	Prenatal Exposure Delayed Effects; Sensitivity and Specificity; Software;
	Street Drugs},
  owner = {stnava},
  pii = {S1053-8119(07)00327-8},
  pmid = {17512218},
  timestamp = {2008.03.22},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2007.04.009}
}

@INCOLLECTION{Gee2005,
  author = {Gee, J.C. and Zhang, H. and Dubb, A. and Avants, B. and Yushkevich,
	P. and Duda, J.T.},
  title = {Anatomy-based visualizations of diffusion tensor images of brain
	white matter},
  booktitle = {Visualization and Image Processing of Tensor Fields},
  publisher = {Springer},
  year = {2005},
  editor = {Weickert, J. and Hagan, H.},
  address = {Berlin}
}

@ARTICLE{M.Grossman10282008,
  author = {Grossman, M. and Anderson, C. and Khan, A. and Avants, B. and Elman,
	L. and McCluskey, L.},
  title = {{Impaired action knowledge in amyotrophic lateral sclerosis}},
  journal = {Neurology},
  year = {2008},
  volume = {71},
  pages = {1396-1401},
  number = {18},
  abstract = {Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative
	condition affecting the motor system, but recent work also shows
	more widespread cognitive impairment. This study examined performance
	on measures requiring knowledge of actions, and related performance
	to MRI cortical atrophy in ALS. Methods: A total of 34 patients with
	ALS performed measures requiring word-description matching and associativity
	judgments with actions and objects. Voxel-based morphometry was used
	to relate these measures to cortical atrophy using high resolution
	structural MRI. Results: Patients with ALS were significantly more
	impaired on measures requiring knowledge of actions than measures
	requiring knowledge of objects. Difficulty on measures requiring
	action knowledge correlated with cortical atrophy in motor cortex,
	implicating degraded knowledge of action features represented in
	motor cortex of patients with ALS. Performance on measures requiring
	object knowledge did not correlate with motor cortex atrophy. Several
	areas correlated with difficulty for both actions and objects, implicating
	these brain areas in components of semantic memory that are not dedicated
	to a specific category of knowledge. Conclusion: Patients with amyotrophic
	lateral sclerosis are impaired on measures involving action knowledge,
	and this appears to be due to at least two sources of impairment:
	degradation of knowledge about action features represented in motor
	cortex and impairment on multicategory cognitive components contributing
	more generally to semantic memory.},
  doi = {10.1212/01.wnl.0000319701.50168.8c},
  eprint = {http://www.neurology.org/cgi/reprint/71/18/1396.pdf},
  url = {http://www.neurology.org/cgi/content/abstract/71/18/1396}
}

@ARTICLE{Kim2008,
  author = {Junghoon Kim and Brian Avants and Sunil Patel and John Whyte and
	Branch H Coslett and John Pluta and John A Detre and James C Gee},
  title = {Structural consequences of diffuse traumatic brain injury: A large
	deformation tensor-based morphometry study.},
  journal = {Neuroimage},
  year = {2008},
  volume = {39},
  pages = {1014--1026},
  number = {3},
  month = {Feb},
  abstract = {Traumatic brain injury (TBI) is one of the most common causes of long-term
	disability. Despite the importance of identifying neuropathology
	in individuals with chronic TBI, methodological challenges posed
	at the stage of inter-subject image registration have hampered previous
	voxel-based MRI studies from providing a clear pattern of structural
	atrophy after TBI. We used a novel symmetric diffeomorphic image
	normalization method to conduct a tensor-based morphometry (TBM)
	study of TBI. The key advantage of this method is that it simultaneously
	estimates an optimal template brain and topology preserving deformations
	between this template and individual subject brains. Detailed patterns
	of atrophies are then revealed by statistically contrasting control
	and subject deformations to the template space. Participants were
	29 survivors of TBI and 20 control subjects who were matched in terms
	of age, gender, education, and ethnicity. Localized volume losses
	were found most prominently in white matter regions and the subcortical
	nuclei including the thalamus, the midbrain, the corpus callosum,
	the mid- and posterior cingulate cortices, and the caudate. Significant
	voxel-wise volume loss clusters were also detected in the cerebellum
	and the frontal/temporal neocortices. Volume enlargements were identified
	largely in ventricular regions. A similar pattern of results was
	observed in a subgroup analysis where we restricted our analysis
	to the 17 TBI participants who had no macroscopic focal lesions (total
	lesion volume >1.5 cm(3)). The current study confirms, extends, and
	partly challenges previous structural MRI studies in chronic TBI.
	By demonstrating that a large deformation image registration technique
	can be successfully combined with TBM to identify TBI-induced diffuse
	structural changes with greater precision, our approach is expected
	to increase the sensitivity of future studies examining brain-behavior
	relationships in the TBI population.},
  doi = {05},
  institution = {Moss Rehabilitation Research Institute, Albert Einstein Healthcare
	Network, Philadelphia, PA, USA.},
  owner = {stnava},
  pii = {S1053-8119(07)00901-9},
  pmid = {17999940},
  timestamp = {2008.02.25},
  url = {http://dx.doi.org/05}
}

@ARTICLE{Klein2009,
  author = {Arno Klein and Jesper Andersson and Babak A Ardekani and John Ashburner
	and Brian Avants and Ming-Chang Chiang and Gary E Christensen and
	D. Louis Collins and James Gee and Pierre Hellier and Joo Hyun Song
	and Mark Jenkinson and Claude Lepage and Daniel Rueckert and Paul
	Thompson and Tom Vercauteren and Roger P Woods and J. John Mann and
	Ramin V Parsey},
  title = {Evaluation of 14 nonlinear deformation algorithms applied to human
	brain MRI registration.},
  journal = {Neuroimage},
  year = {2009},
  volume = {46},
  pages = {786--802},
  number = {3},
  month = {Jul},
  abstract = {All fields of neuroscience that employ brain imaging need to communicate
	their results with reference to anatomical regions. In particular,
	comparative morphometry and group analysis of functional and physiological
	data require coregistration of brains to establish correspondences
	across brain structures. It is well established that linear registration
	of one brain to another is inadequate for aligning brain structures,
	so numerous algorithms have emerged to nonlinearly register brains
	to one another. This study is the largest evaluation of nonlinear
	deformation algorithms applied to brain image registration ever conducted.
	Fourteen algorithms from laboratories around the world are evaluated
	using 8 different error measures. More than 45,000 registrations
	between 80 manually labeled brains were performed by algorithms including:
	AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO,
	SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular
	Normalization, Unified Segmentation, and the DARTEL Toolbox). All
	of these registrations were preceded by linear registration between
	the same image pairs using FLIRT. One of the most significant findings
	of this study is that the relative performances of the registration
	methods under comparison appear to be little affected by the choice
	of subject population, labeling protocol, and type of overlap measure.
	This is important because it suggests that the findings are generalizable
	to new subject populations that are labeled or evaluated using different
	labeling protocols. Furthermore, we ranked the 14 methods according
	to three completely independent analyses (permutation tests, one-way
	ANOVA tests, and indifference-zone ranking) and derived three almost
	identical top rankings of the methods. ART, SyN, IRTK, and SPM's
	DARTEL Toolbox gave the best results according to overlap and distance
	measures, with ART and SyN delivering the most consistently high
	accuracy across subjects and label sets. Updates will be published
	on the http://www.mindboggle.info/papers/ website.},
  doi = {10.1016/j.neuroimage.2008.12.037},
  institution = {New York State Psychiatric Institute, Columbia University, NY, NY
	10032, USA. arno@binarybottle.com},
  owner = {stnava},
  pii = {S1053-8119(08)01297-4},
  pmid = {19195496},
  timestamp = {2009.05.28},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2008.12.037}
}

@ARTICLE{Massimo2009,
  author = {Lauren Massimo and Chivon Powers and Peachie Moore and Luisa Vesely
	and Brian Avants and James Gee and David J Libon and Murray Grossman},
  title = {Neuroanatomy of apathy and disinhibition in frontotemporal lobar
	degeneration.},
  journal = {Dement Geriatr Cogn Disord},
  year = {2009},
  volume = {27},
  pages = {96--104},
  number = {1},
  abstract = {OBJECTIVE: To investigate the neural basis for the behavioral symptoms
	of frontotemporal lobar degeneration (FTLD) that cause the greatest
	caregiver distress. BACKGROUND: FTLD is a progressive neurodegenerative
	disease associated with behavioral disturbances. Group studies have
	related these behaviors to volume loss on MRI. METHODS: Forty caregivers
	of patients with the clinical diagnosis of FTLD completed the Neuropsychiatric
	Inventory. Twelve neuropsychiatric symptoms and the associated caregiver
	distress were assessed. Optimized voxel-based morphometry identified
	significant atrophy in subgroups of FTLD patients with isolated behavioral
	symptoms corresponding to the most distressing behaviors, and we
	correlated cortical atrophy directly with these distressing behavioral
	disorders in an unbiased group analysis. RESULTS: The greatest stressors
	for caregivers were apathy and disinhibition (p < 0.005 for both
	contrasts). Partially distinct areas of cortical atrophy were associated
	with these behaviors in both individual patients with these symptoms
	and group-wide analyses, including the dorsal anterior cingulate
	cortex and dorsolateral prefrontal cortex in apathetic patients,
	and the medial orbital frontal cortex in disinhibited patients. CONCLUSIONS:
	Caregiver stress in families of FTLD patients is due in large part
	to apathy and disinhibition. The anatomic distribution of cortical
	loss corresponding to these distressing social behaviors includes
	partially distinct areas within the frontal lobe.},
  doi = {10.1159/000194658},
  institution = {Department of Neurology, University of Pennsylvania School of Medicine,
	Philadelphia, Pa., USA.},
  owner = {stnava},
  pii = {000194658},
  pmid = {19158440},
  timestamp = {2009.02.14},
  url = {http://dx.doi.org/10.1159/000194658}
}

@ARTICLE{Ng2007,
  author = {Lydia Ng and Sayan D Pathak and Chihchau Kuan and Chris Lau and Hongwei
	Dong and Andrew Sodt and Chinh Dang and Brian Avants and Paul Yushkevich
	and James C Gee and David Haynor and Ed Lein and Allan Jones and
	Mike Hawrylycz},
  title = {Neuroinformatics for genome-wide 3D gene expression mapping in the
	mouse brain.},
  journal = {IEEE/ACM Trans Comput Biol Bioinform},
  year = {2007},
  volume = {4},
  pages = {382--393},
  number = {3},
  abstract = {Large scale gene expression studies in the mammalian brain offer the
	promise of understanding the topology, networks and ultimately the
	function of its complex anatomy, opening previously unexplored avenues
	in neuroscience. High-throughput methods permit genome-wide searches
	to discover genes that are uniquely expressed in brain circuits and
	regions that control behavior. Previous gene expression mapping studies
	in model organisms have employed situ hybridization (ISH), a technique
	that uses labeled nucleic acid probes to bind to specific mRNA transcripts
	in tissue sections. A key requirement for this effort is the development
	of fast and robust algorithms for anatomically mapping and quantifying
	gene expression for ISH. We describe a neuroinformatics pipeline
	for automatically mapping expression profiles of ISH data and its
	use to produce the first genomic scale 3-D mapping of gene expression
	in a mammalian brain. The pipeline is fully automated and adaptable
	to other organisms and tissues. Our automated study of over 20,000
	genes indicates that at least 78.8 percent are expressed at some
	level in the adult C56BL/6J mouse brain. In addition to providing
	a platform for genomic scale search, high-resolution images and visualization
	tools for expression analysis are available at the Allen Brain Atlas
	web site (http://www.brain-map.org).},
  doi = {10.1109/tcbb.2007.1035},
  institution = {Allen Institute for Brain Science, Seattle, WA 98103, USA. lydian@alleninstitute.org},
  keywords = {Algorithms; Animals; Brain; Chromosome Mapping; Computational Biology;
	Gene Expression Profiling; Imaging, Three-Dimensional; In Situ Hybridization,
	Fluorescence; Male; Mice; Mice, Inbred C57BL; Microscopy, Fluorescence;
	Nerve Tissue Proteins; Neurosciences},
  owner = {stnava},
  pmid = {17666758},
  timestamp = {2008.05.29},
  url = {http://dx.doi.org/10.1109/tcbb.2007.1035}
}

@ARTICLE{Pluta2008,
  author = {J. Pluta and B. Avants and P. Yushkevich and S. Glynn and S. Awate
	and J. Detre and D. Mechanic and J. C. Gee},
  title = {Expectation matching for incomplete label driven semi-automated hippocampus
	segmentation in epilepsy},
  journal = {Hippocampus},
  year = {2009},
  pages = {accepted},
  owner = {stnava},
  timestamp = {2008.10.06}
}

@INPROCEEDINGS{Rao2006,
  author = {H. Rao and H. Hurt and M. Korczykowski and J. Giannetta and B. B.
	Avants and J. C. Gee and J. A. Detre and J. Wang},
  title = {Altered Resting Brain Function in Prenatally Cocaine-exposed Teenagers:
	A CASL Perfusion fMRI Study},
  booktitle = {ISMRM 2006},
  year = {2006}
}

@ARTICLE{Rao2007,
  author = {Hengyi Rao and Jiongjiong Wang and Joan Giannetta and Marc Korczykowski
	and David Shera and Brian B Avants and James Gee and John A Detre
	and Hallam Hurt},
  title = {Altered resting cerebral blood flow in adolescents with in utero
	cocaine exposure revealed by perfusion functional MRI.},
  journal = {Pediatrics},
  year = {2007},
  volume = {120},
  pages = {e1245--e1254},
  number = {5},
  month = {Nov},
  abstract = {OBJECTIVES: Animal studies have clearly demonstrated the effects of
	in utero cocaine exposure on neural ontogeny, especially in dopamine-rich
	areas of cerebral cortex; however, less is known about how in utero
	cocaine exposure affects longitudinal neurocognitive development
	of the human brain. We used continuous arterial spin-labeling perfusion
	functional MRI to measure the effect of in utero cocaine exposure
	on resting brain function by comparing resting cerebral blood flow
	of cocaine-exposed adolescents with non-cocaine-exposed control subjects.
	PATIENTS AND METHODS: Twenty-four cocaine-exposed adolescents and
	25 matched non-cocaine-exposed control subjects underwent structural
	and perfusion functional MRI during resting states. Direct subtraction,
	voxel-wise general linear modeling, and region-of-interest analyses
	were performed on the cerebral blood flow images to compare the resting
	cerebral blood flow between the 2 groups. RESULTS: Compared with
	control subjects, cocaine-exposed adolescents showed significantly
	reduced global cerebral blood flow. The decrease of cerebral blood
	flow in cocaine-exposed adolescents was observed mainly in posterior
	and inferior brain regions, including the occipital cortex and thalamus.
	After adjusting for global cerebral blood flow, however, a significant
	increase in relative cerebral blood flow in cocaine-exposed adolescents
	was found in anterior and superior brain regions, including the prefrontal,
	cingulate, insular, amygdala, and superior parietal cortex. Furthermore,
	the functional modulations by in utero cocaine exposure on all of
	these regions except amygdala cannot be accounted for by the variation
	in brain anatomy. CONCLUSIONS: In utero cocaine exposure may reduce
	global cerebral blood flow, and this reduction may persist into adolescence.
	The relative increase of cerebral blood flow in anterior and superior
	brain regions in cocaine-exposed adolescent participants suggests
	that compensatory mechanisms for reduced global cerebral blood flow
	may develop during neural ontogeny. Arterial spin-labeling perfusion
	MRI may be a valuable tool for investigating the long-term effects
	of in utero drug exposure.},
  doi = {10.1542/peds.2006-2596},
  institution = {Department of Radiology and Neurology, Center for Functional Neuroimaging,
	University of Pennsylvania, Philadelphia, Pennsylvania, USA.},
  keywords = {Adolescent; Age Factors; Blood Circulation Time; Blood Flow Velocity;
	Cerebrovascular Circulation; Cocaine; Cocaine-Related Disorders;
	Female; Humans; Magnetic Resonance Imaging; Male; Pregnancy; Prenatal
	Exposure Delayed Effects; Rest},
  owner = {stnava},
  pii = {120/5/e1245},
  pmid = {17974718},
  timestamp = {2008.03.22},
  url = {http://dx.doi.org/10.1542/peds.2006-2596}
}

@ARTICLE{Rao2007a,
  author = {H. Rao and J. Wang and M. Korczykowski and J. Giannetta and D. Shera
	and B. Avants and J. Gee and J.A. Detre and H. Hurt},
  title = {Altered Resting Cerebral Blood Flow in Adolescents with In-utero
	Cocaine Exposure Revealed by Perfusion Functional MRI},
  journal = {Pediatrics},
  year = {2007},
  volume = {120},
  pages = {e1245-1254},
  owner = {stnava},
  timestamp = {2007.02.06}
}

@ARTICLE{Schoenemann2007,
  author = {P.T. Schoenemann and J. Gee and B. Avants and R.L. Holloway and J.
	Monge and J. Lewis},
  title = {Validation of plaster endocast morphology through 3D CT image analysis.},
  journal = {Am J Phys Anthropol},
  year = {2007},
  volume = {132},
  pages = {183-92},
  eid = {PMID: 17103425},
  owner = {stnava},
  timestamp = {2007.02.07}
}

@INPROCEEDINGS{Schoenemann2004,
  author = {P. T. Schoenemann and B. B. Avants and J. C. Gee and L. D. Glotzer
	and M. J. Sheehan},
  title = {Analysis of chimp-human brain differences via non-rigid deformation
	of {3D MR} images},
  booktitle = {American Journal of Physical Anthropology},
  year = {2004},
  volume = {123},
  pages = {174-175},
  optnumber = {38}
}

@ARTICLE{Schoenemann2007a,
  author = {P. Thomas Schoenemann and James Gee and Brian Avants and Ralph L
	Holloway and Janet Monge and Jason Lewis},
  title = {Validation of plaster endocast morphology through 3D CT image analysis.},
  journal = {Am J Phys Anthropol},
  year = {2007},
  volume = {132},
  pages = {183--192},
  number = {2},
  month = {Feb},
  abstract = {A crucial component of research on brain evolution has been the comparison
	of fossil endocranial surfaces with modern human and primate endocrania.
	The latter have generally been obtained by creating endocasts out
	of rubber latex shells filled with plaster. The extent to which the
	method of production introduces errors in endocast replicas is unknown.
	We demonstrate a powerful method of comparing complex shapes in 3-dimensions
	(3D) that is broadly applicable to a wide range of paleoanthropological
	questions. Pairs of virtual endocasts (VEs) created from high-resolution
	CT scans of corresponding latex/plaster endocasts and their associated
	crania were rigidly registered (aligned) in 3D space for two Homo
	sapiens and two Pan troglodytes specimens. Distances between each
	cranial VE and its corresponding latex/plaster VE were then mapped
	on a voxel-by-voxel basis. The results show that between 79.7\% and
	91.0\% of the voxels in the four latex/plaster VEs are within 2 mm
	of their corresponding cranial VEs surfaces. The average error is
	relatively small, and variation in the pattern of error across the
	surfaces appears to be generally random overall. However, inferior
	areas around the cranial base and the temporal poles were somewhat
	overestimated in both human and chimpanzee specimens, and the area
	overlaying Broca's area in humans was somewhat underestimated. This
	study gives an idea of the size of possible error inherent in latex/plaster
	endocasts, indicating the level of confidence we can have with studies
	relying on comparisons between them and, e.g., hominid fossil endocasts.},
  doi = {10.1002/ajpa.20499},
  institution = {Department of Behavioral Sciences, University of Michigan-Dearborn,
	Dearborn, MI 48128, USA. ptoms@umd.umich.edu},
  keywords = {Animals; Fossils; Humans; Imaging, Three-Dimensional; Paleontology;
	Pan troglodytes; Skull; Tomography, X-Ray Computed},
  owner = {stnava},
  pmid = {17103425},
  timestamp = {2008.05.29},
  url = {http://dx.doi.org/10.1002/ajpa.20499}
}

@ARTICLE{Simon2008,
  author = {Tony J Simon and Zhongle Wu and Brian Avants and Hui Zhang and James
	C Gee and Glenn T Stebbins},
  title = {Atypical cortical connectivity and visuospatial cognitive impairments
	are related in children with chromosome 22q11.2 deletion syndrome.},
  journal = {Behav Brain Funct},
  year = {2008},
  volume = {4},
  pages = {25},
  abstract = {ABSTRACT: BACKGROUND: Chromosome 22q11.2 deletion syndrome is one
	of the most common genetic causes of cognitive impairment and developmental
	disability yet little is known about the neural bases of those challenges.
	Here we expand upon our previous neurocognitive studies by specifically
	investigating the hypothesis that changes in neural connectivity
	relate to cognitive impairment in children with the disorder. METHODS:
	Whole brain analyses of multiple measures computed from diffusion
	tensor image data acquired from the brains of children with the disorder
	and typically developing controls. We also correlated diffusion tensor
	data with performance on a visuospatial cognitive task that taps
	spatial attention. RESULTS: Analyses revealed four common clusters,
	in the parietal and frontal lobes, that showed complementary patterns
	of connectivity in children with the deletion and typical controls.
	We interpreted these results as indicating differences in connective
	complexity to adjoining cortical regions that are critical to the
	cognitive functions in which affected children show impairments.
	Strong, and similarly opposing patterns of correlations between diffusion
	values in those clusters and spatial attention performance measures
	considerably strengthened that interpretation. CONCLUSION: Our results
	suggest that atypical development of connective patterns in the brains
	of children with chromosome 22q11.2 deletion syndrome indicate a
	neuropathology that is related to the visuospatial cognitive impairments
	that are commonly found in affected individuals.},
  doi = {10.1186/1744-9081-4-25},
  institution = {M,I,N,D, Institute, University of California, Davis, 2825 50th Street,
	Sacramento, CA 95817, USA. tjsimon@ucdavis.edu.},
  owner = {stnava},
  pii = {1744-9081-4-25},
  pmid = {18559106},
  timestamp = {2008.10.06},
  url = {http://dx.doi.org/10.1186/1744-9081-4-25}
}

@ARTICLE{Song2006,
  author = {Zhuang Song and Nicholas Tustison and Brian Avants and James C Gee},
  title = {Integrated graph cuts for brain MRI segmentation.},
  journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput
	Assist Interv},
  year = {2006},
  volume = {9},
  pages = {831--838},
  number = {Pt 2},
  abstract = {Brain MRI segmentation remains a challenging problem in spite of numerous
	existing techniques. To overcome the inherent difficulties associated
	with this segmentation problem, we present a new method of information
	integration in a graph based framework. In addition to image intensity,
	tissue priors and local boundary information are integrated into
	the edge weight metrics in the graph. Furthermore, inhomogeneity
	correction is incorporated by adaptively adjusting the edge weights
	according to the intermediate inhomogeneity estimation. In the validation
	experiments of simulated brain MRIs, the proposed method outperformed
	a segmentation method based on iterated conditional modes (ICM),
	which is a commonly used optimization method in medical image segmentation.
	In the experiments of real neonatal brain MRIs, the results of the
	proposed method have good overlap with the manual segmentations by
	human experts.},
  institution = {Penn Image Computing and Science Lab, University of Pennsylvania,
	USA. songz@seas.upenn.edu},
  keywords = {Algorithms; Artificial Intelligence; Brain; Humans; Image Enhancement;
	Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional;
	Magnetic Resonance Imaging; Pattern Recognition, Automated; Reproducibility
	of Results; Sensitivity a; nd Specificity},
  owner = {stnava},
  pmid = {17354850},
  timestamp = {2008.05.29}
}

@INPROCEEDINGS{Song2006a,
  author = {Zhuang Song and Nicholas J. Tustison and Brian B. Avants and James
	C. Gee},
  title = {Adaptive graph cuts with tissue priors for brain MRI segmentation.},
  booktitle = {ISBI},
  year = {2006},
  pages = {762-765}
}

@ARTICLE{Sun2008,
  author = {Hui Sun and Brian B Avants and Alejandro F Frangi and Federico Sukno
	and James C Geel and Paul A Yushkevich},
  title = {Cardiac medial modeling and time-course heart wall thickness analysis.},
  journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput
	Assist Interv},
  year = {2008},
  volume = {11},
  pages = {766--773},
  number = {Pt 2},
  abstract = {The medial model is a powerful shape representation method that models
	a 3D object by explicitly defining its skeleton (medial axis) and
	deriving the boundary geometry according to medial geometry. It has
	been recently extended to model complex shapes with multi-figures,
	i.e., shapes whose skeletons can not be described by a single sheet
	in 3D. This paper applied the medial model to a 2-chamber heart data
	set consisting of 428 cardiac shapes from 90 subjects. The results
	show that the medial model can capture the heart shape accurately.
	To demonstrate the usage of the medial model, the changes of the
	heart wall thickness over time are analyzed. We calculated the mean
	heart wall thickness map of 90 subjects for different phases of the
	cardiac cycle, as well as the mean thickness change between phases.},
  institution = {Department of Radiology, University of Pennsylvania, Philadelphia,
	PA, USA.},
  keywords = {Algorithms; Computer Simulation; Humans; Image Enhancement; Image
	Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic
	Resonance Imaging; Models, Cardiovascular; Myocardium; Reproducibility
	of Results; Sensitivity and Specificity; Subtraction Technique},
  owner = {stnava},
  pmid = {18982674},
  timestamp = {2009.02.14}
}

@ARTICLE{Sundaram2005,
  author = {Tessa A Sundaram and Brian B Avants and James C Gee},
  title = {Towards a dynamic model of pulmonary parenchymal deformation: evaluation
	of methods for temporal reparameterization of lung data.},
  journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput
	Assist Interv},
  year = {2005},
  volume = {8},
  pages = {328--335},
  number = {Pt 2},
  abstract = {We approach the problem of temporal reparameterization of dynamic
	sequences of lung MR images. In earlier work, we employed capacity-based
	reparameterization to co-register temporal sequences of 2-D coronal
	images of the human lungs. Here, we extend that work to the evaluation
	of a ventilator-acquired 3-D dataset from a normal mouse. Reparameterization
	according to both deformation and lung volume is evaluated. Both
	measures provide results that closely approximate normal physiological
	behavior, as judged from the original data. Our ultimate goal is
	to be able to characterize normal parenchymal biomechanics over a
	population of healthy individuals, and to use this statistical model
	to evaluate lung deformation under various pathological states.},
  institution = {University of Pennsylvania, Philadelphia PA 19104, USA.},
  keywords = {Algorithms; Animals; Computer Simulation; Databases, Factual; Elasticity;
	Image Enhancement; Image Interpretation, Computer-Assisted; Imaging,
	Three-Dimensional; Lung; Magnetic Resonance Imaging; Mice; Models,
	Biological; Reproducibility of Results; Respiratory Mechanics; Sensitivity
	and Specificity; Subtraction Technique},
  owner = {stnava},
  pmid = {16685976},
  timestamp = {2008.05.29}
}

@ARTICLE{Tustison2009,
  author = {Nicholas J Tustison and Brian B Avants and James C Gee},
  title = {Directly manipulated free-form deformation image registration.},
  journal = {IEEE Trans Image Process},
  year = {2009},
  volume = {18},
  pages = {624--635},
  number = {3},
  month = {Mar},
  abstract = {Previous contributions to both the research and open source software
	communities detailed a generalization of a fast scalar field fitting
	technique for cubic B-splines based on the work originally proposed
	by Lee . One advantage of our proposed generalized B-spline fitting
	approach is its immediate application to a class of nonrigid registration
	techniques frequently employed in medical image analysis. Specifically,
	these registration techniques fall under the rubric of free-form
	deformation (FFD) approaches in which the object to be registered
	is embedded within a B-spline object. The deformation of the B-spline
	object describes the transformation of the image registration solution.
	Representative of this class of techniques, and often cited within
	the relevant community, is the formulation of Rueckert who employed
	cubic splines with normalized mutual information to study breast
	deformation. Similar techniques from various groups provided incremental
	novelty in the form of disparate explicit regularization terms, as
	well as the employment of various image metrics and tailored optimization
	methods. For several algorithms, the underlying gradient-based optimization
	retained the essential characteristics of Rueckert's original contribution.
	The contribution which we provide in this paper is two-fold: 1) the
	observation that the generic FFD framework is intrinsically susceptible
	to problematic energy topographies and 2) that the standard gradient
	used in FFD image registration can be modified to a well-understood
	preconditioned form which substantially improves performance. This
	is demonstrated with theoretical discussion and comparative evaluation
	experimentation.},
  doi = {10.1109/TIP.2008.2010072},
  owner = {stnava},
  pmid = {19171516},
  timestamp = {2009.02.14},
  url = {http://dx.doi.org/10.1109/TIP.2008.2010072}
}

@ARTICLE{Yushkevich2009,
  author = {Paul A Yushkevich and Brian B Avants and John Pluta and Sandhitsu
	Das and David Minkoff and Dawn Mechanic-Hamilton and Simon Glynn
	and Stephen Pickup and Weixia Liu and James C Gee and Murray Grossman
	and John A Detre},
  title = {A high-resolution computational atlas of the human hippocampus from
	postmortem magnetic resonance imaging at 9.4 T.},
  journal = {Neuroimage},
  year = {2009},
  volume = {44},
  pages = {385--398},
  number = {2},
  month = {Jan},
  abstract = {This paper describes the construction of a computational anatomical
	atlas of the human hippocampus. The atlas is derived from high-resolution
	9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus
	(cornu ammonis fields CA1, CA2/3; the dentate gyrus; and the vestigial
	hippocampal sulcus) are labeled in the images manually using a combination
	of distinguishable image features and geometrical features. A synthetic
	average image is derived from the MRI of the samples using shape
	and intensity averaging in the diffeomorphic non-linear registration
	framework, and a consensus labeling of the template is generated.
	The agreement of the consensus labeling with manual labeling of each
	sample is measured, and the effect of aiding registration with landmarks
	and manually generated mask images is evaluated. The atlas is provided
	as an online resource with the aim of supporting subfield segmentation
	in emerging hippocampus imaging and image analysis techniques. An
	example application examining subfield-level hippocampal atrophy
	in temporal lobe epilepsy demonstrates the application of the atlas
	to in vivo studies.},
  doi = {10.1016/j.neuroimage.2008.08.042},
  institution = {Penn Image Computing and Science Laboratory (PICSL), Department of
	Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
	pauly2@mail.med.upenn.edu},
  owner = {stnava},
  pii = {S1053-8119(08)00976-2},
  pmid = {18840532},
  timestamp = {2009.02.14},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2008.08.042}
}

@ARTICLE{Yushkevich2008,
  author = {Paul A Yushkevich and Brian B Avants and John Pluta and David Minkoff
	and John A Detre and Murray Grossman and James C Gee},
  title = {Shape-based alignment of hippocampal subfields: evaluation in postmortem
	MRI.},
  journal = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput
	Assist Interv},
  year = {2008},
  volume = {11},
  pages = {510--517},
  number = {Pt 1},
  abstract = {This paper estimates the accuracy of hippocampal subfield alignment
	via shape-based normalization. Evaluation takes place in postmortem
	MRI dataset acquired at 9.4 Tesla with many averages and approximately
	0.01 mm3 voxel resolution. Continuous medial representations (cm-reps)
	are used to establish geometrical correspondences between hippocampal
	formations in different images; the extent to which these correspondences
	match up subfields is evaluated and compared to normalization driven
	by image forces. Shape-based normalization is shown to perform only
	slightly worse than image-based normalization; this is encouraging
	because the former is more applicable to in vivo MRI, which typically
	lacks features that distinguish hippocampal subfields.},
  institution = {Department of Radiology, University of Pennsylvania, USA.},
  keywords = { of Results; Algorithms; Artificial Intelligence; Cadaver; Computer
	Simulation; Hippocampus; Humans; Image Enhancement; Image Interpretation,
	Computer-Assisted; Information Storage and Retrieval; Magnetic Resonance
	Imaging; Models, Biological; Models, Statistical; Pattern Recogni;
	Reproducibility; Sensitivity and Specificity; Subtraction Technique;
	tion, Automated},
  owner = {stnava},
  pmid = {18979785},
  timestamp = {2009.02.14}
}

@ARTICLE{Zhang2007,
  author = {Hui Zhang and Brian B Avants and Paul A Yushkevich and John H Woo
	and Sumei Wang and Leo F McCluskey and Lauren B Elman and Elias R
	Melhem and James C Gee},
  title = {High-dimensional spatial normalization of diffusion tensor images
	improves the detection of white matter differences: an example study
	using amyotrophic lateral sclerosis.},
  journal = {IEEE Trans Med Imaging},
  year = {2007},
  volume = {26},
  pages = {1585--1597},
  number = {11},
  month = {Nov},
  abstract = {Spatial normalization of diffusion tensor images plays a key role
	in voxel-based analysis of white matter (WM) group differences. Currently,
	it has been achieved using low-dimensional registration methods in
	the large majority of clinical studies. This paper aims to motivate
	the use of high-dimensional normalization approaches by generating
	evidence of their impact on the findings of such studies. Using an
	ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three
	normalization methods representing the current range of available
	approaches: low-dimensional normalization using the fractional anisotropy
	(FA), high-dimensional normalization using the FA, and high-dimensional
	normalization using full tensor information. Each method was assessed
	in terms of its ability to detect significant differences between
	ALS patients and controls. Our findings suggest that inadequate normalization
	with low-dimensional approaches can result in insufficient removal
	of shape differences which in turn can confound FA differences in
	a complex manner, and that utilizing high-dimensional normalization
	can both significantly minimize the confounding effect of shape differences
	to FA differences and provide a more complete description of WM differences
	in terms of both size and tissue architecture differences. We also
	found that high-dimensional approaches, by leveraging full tensor
	features instead of tensor-derived indices, can further improve the
	alignment of WM tracts.},
  institution = {Penn Image Computing and Science Laboratory, University of Pennsylvania,
	Philadelphia, PA 19104, USA.},
  keywords = {Adult; Aged; Algorithms; Amyotrophic Lateral Sclerosis; Artificial
	Intelligence; Brain; Diffusion Magnetic Resonance Imaging; Female;
	Humans; Image Enhancement; Image Interpretation, Computer-Assisted;
	Imaging, Three-Dimensional; Male; Middle Aged; Nerve Fibers, Myelinated;
	Pattern Recognition, Automated; Reproducibility of Results; Sensitivity
	and Specificity},
  owner = {stnava},
  pmid = {18041273},
  timestamp = {2008.03.22}
}

@INBOOK{Yoo2004,
  chapter = {Non-rigid registration},
  title = {Insight Into Images: Theory for Segmentation, Registration and Image
	Analysis %Insight Into Images Principles and Practice for Segmentation,
	Registration and Image Analysis: Theory},
  publisher = {A. K. Peters Ltd., Natick, MA},
  year = {2004},
  editor = {T. Yoo},
  note = {primary author of this chapter}
}

